大数据下学习流访问控制安全模型及算法研究

Research on Learning Flow Access Control Security Model and Algorithm in Big Data Environment

随着大数据和移动网络的迅速普及,教育大数据云服务及移动智能学习朝着规模化的方向发展,相应的学习流技术及许多安全问题亟待突破.访问控制是安全机制的核心问题,为了保证大数据环境下学习流的安全性和授权的有效性,选择以角色为基础的访问控制技术,进行学习流访问控制的模型构建及改进扩展,并进一步建立基于动态控制机制的学习流安全模型,然后综合多种权限控制方式,提出基于大数据云服务的学习流访问控制安全模型,接着设计相关角色查找算法并予以优化,提出改进角色查找算法IRSA.所构建的几种模型开放、一致、实用,能保护学习资源不被非法使用和访问,适合大数据环境下复杂学习流程控制的安全需要,支持云端移动学习所要求的安全访问.优化算法能够更有效减少学习流访问控制角色指派数及角色指派响应时间,降低角色授权安全冲突.

With the rapid popularization of big data and mobile networks, education for big data cloud services and mobile intelligent learning are developing in scale. Corresponding learning flow technologies and many security issues need to be urgently resolved.Access control is the core issue of security mechanisms. In order to ensure the security of learning flow and the effectiveness of authorization under the big data environment, role-based access control technology is selected to implement model construction and improvement of learning flow access control. And further study to establish security model based on dynamic flow control mechanism.Then a multiple access control method is integrated, a learning flow access control security model based on big data cloud service is proposed. Then, a related role search algorithm is designed and optimized,and an improved role finding algorithm IRSA is proposed. The models are open, consistent, and practical. They can prevent learning resources from being illegally used and accessed. They are suitable for the security needs of complex learning process control in big data environments and support the secure access required by mobile learning in the cloud. The optimization algorithm can more effectively reduce the number of role assignments and role assignment response time for learning flow access control and reduce role authorization security conflicts.